Any real-world thing — a person, place, organization, work of art — is an example of a named entity. Named entities are found everywhere.

Correct pronunciation of named entities is required from many systems — for example, applications like Google Maps that synthesize navigation instructions for drivers using text-to-speech.

Pronunciation of named entities is one of the biggest challenges for speech technologies. Due to their large number, named entities are often excluded from pronunciation lexicons. When processing out-of-vocabulary named entities, G2P engines will often output erroneous transcriptions. As a result, synthetic speech simply mispronounces names.

What makes named entity pronunciation difficult? Firstly, names can be of very diverse etymological origin and can surface in another language without having undergone the process of assimilation. Some street names are good examples of this: Karangahape Road, Tangihua Street, Ngaoho Place. Secondly, name pronunciation is known to be idiosyncratic; there are many pronunciations contradicting common phonological patterns. Consider English city names such as Leicester and Worcester. Thirdly, it's not uncommon for certain names to have different pronunciations when they refer to different things. A famous example of this is the pronunciation of Houston Street in NY vs. Houston, TX.

For most text-to-speech systems, no guess ensures the correct pronunciation better than a direct hit in a pronunciation dictionary. The Cofactor Ora pronunciation lexicon is based on the Google Knowledge Graph. This corpus provides far better coverage of names than any other dictionary.